In Figure REF , we report the combined average accuracy of all models and imbalance strategies against different backbones (Conv4, Conv6, ResNet10, ResNet34). Overall, deeper backbones seem to perform slightly better on the imbalanced tasks, suggesting a higher tolerance for imbalance. For instance, using Conv4 gave \(-7.2\%\)  difference between the balanced and the 1-9shot random task, while using ResNet10 the gap is smaller (\(-6.0\%\) ). The performance degradation observed with ResNet34 is similar to that reported by [1]}, and is most likely caused by the intrinsic instability of meta-training routines on larger backbones.
In Appendix REF , we breakdown the results across different models and training strategies.
